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A Novel Approach for Mining Similarity Profiled Temporal Association Patterns Using Venn Diagrams

Authors:

Vangipuram Radhakrishna,

V. JanakiAuthors Info & Claims

ICEMIS '15: Proceedings of the The International Conference on Engineering & MIS 2015

Article No.: 58, Pages 1 - 9

https://doi.org/10.1145/2832987.2833071

Published: 24 September 2015 Publication History

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Abstract

The problem of mining frequent patterns in a static database is studied extensively in the literature by many researchers. Conventional frequent pattern algorithms are not applicable to find frequent patterns from the temporal database. Temporal database is a database which can store past, present and future information. A temporal relation may be viewed as a database of time invariant and time variant relation instances. The objective of this research is to come up with a novel approach so as to find the temporal association patterns similar to a given reference support sequence and user defined threshold using the concept of Venn diagrams. The proposed approach scans the temporal database only once to find the temporal association patterns and hence reduces the huge overhead incurred when the database is scanned multiple times.

References

[1]

Srivatsan Laxman, P S Sastry. A survey of temporal data mining. Sadhana Vol. 31, Part 2, April 2006, pp. 173--198

[2]

Matteo Golfarelli, Stefano Rizzi. A Survey on Temporal datawarehousing. International Journal of DataWarehousing and Mining, 5(1), 1--7, 2009.

[3]

Gultekin Ozsoyoilu and Richard T. Snodgrass. Temporal and Real-Time Databases: A Survey. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 7, NO. 4, 1995

Digital Library

[4]

Alexander Hinneburg, Dirk Habich, and Wolfgang Lehner. 2003. COMBI-operator - database support for data mining applications. In Proceedings of the 29th international conference on Very large data bases - Volume 29 (VLDB '03), Johann Christoph Freytag, Peter C. Lockemann, Serge Abiteboul, Michael J. Carey, Patricia G. Selinger, and Andreas Heuer (Eds.), Vol. 29. VLDB Endowment 429--439.

Digital Library

[5]

Chang-Hung Lee; Cheng-Ru Lin; Ming-Syan Chen, "On mining general temporal association rules in a publication database," in Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on, vol., no., pp.337--344, 2001

Digital Library

[6]

ATansel Clifford, Gadia JaJodia, Segeu Snodgaass. Temporal Databases. Theory, Design and Implementation. Benjamin Cummings Publishing. 1993

Digital Library

[7]

Tansel, A.U.; Imberman, S.P., "Discovery of Association Rules in Temporal Databases," in Information Technology, 2007. ITNG '07. Fourth International Conference on, vol., no., pp.371--376, 2--4 April 2007

Digital Library

[8]

Toon Calders. Deducing Bounds on the Frequents of Itemsets.

[9]

Artur Bykowski, Jouni K. Seppanen, Jaakko Hollomen. Model Independent bounding of the supports of Boolean formulae in binary data. Database Support for Data Mining Applications Lecture Notes in Computer Science Volume 2682, 2004, pp 234--249

[10]

Toon Calders, Jan Paredaens. Axiomatization of frequent items. Theoretical Computer Science. Volume 290, Issue 1, 1 January 2003, Pages 669--693

Digital Library

[11]

Panagiotis Papapetrou, George Kollios, Stan Sclaroff, Dimitrios Gunopulos. Mining frequent arrangements of temporal intervals. Knowledge and Information Systems. November 2009, Volume 21, Issue 2, pp 133--171

Digital Library

[12]

Long Jin, Yongmi Lee, Sungbo Seo, Keun Ho Ryu. Discovery of Temporal Frequent Patterns Using TFP-Tree. Advances in Web-Age Information Management Lecture Notes in Computer Science Volume 40, Issue 16, 2006, pp 349--361

Digital Library

[13]

Cheqing Jinet.al. Mining Frequent Items in Spatio-temporal Databases. Advances in Web-Age Information Management Lecture Notes in Computer Science Volume 31, Issue 29, 2004, pp 549--558

[14]

Jin Soung Yoo. Temporal Data Mining : Similarity profiled association pattern. Data Mining Found & Intel paradigms. Pg 29--47

[15]

Jin Soung Yoo, Shashi Sekhar Mining Temporal association patterns under a similarity constraint. Scientific and Statistical Database Management Lecture Notes in Computer Science Volume 5069, 2008, pp 401--417, Springer.

Digital Library

[16]

Jin Soung Yoo; Shashi Shekhar, "Similarity-Profiled Temporal Association Mining," in Knowledge and Data Engineering, IEEE Transactions on, vol.21, no.8, pp.1147--1161, Aug. 2009

Digital Library

Cited By

Bandaru RPola SThadem SPendyala KVangipuram RVangipuram S(2021)Design and Analysis of activation functions used in deep learning modelsThe 7th International Conference on Engineering & MIS 202110.1145/3492547.3492575(1-5)Online publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1145/3492547.3492575
Cheruvu AVangipuram R(2021)A Survey of Similarity Measures for Time stamped Temporal DatasetsInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460754(193-197)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460754
M.S.B PC.V GVangipuram RCheruvu A(2021)Similarity Association Pattern Mining in Transaction DatabasesInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460752(180-184)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460752
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Index Terms

A Novel Approach for Mining Similarity Profiled Temporal Association Patterns Using Venn Diagrams

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Published In

cover image ACM Other conferences

ICEMIS '15: Proceedings of the The International Conference on Engineering & MIS 2015

September 2015

429 pages

ISBN:9781450334181

DOI:10.1145/2832987

Conference Chair:
Shadi Aljawarneh,
Program Chair:
Bassam Al-Shargabi

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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The Isra University
University of Aizu: University of Aizu
IBM: IBM

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 September 2015

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ICEMIS '15

ICEMIS '15: The International Conference on Engineering & MIS 2015

September 24 - 26, 2015

Istanbul, Turkey

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Overall Acceptance Rate 215 of 605 submissions, 36%

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Cited By

Bandaru RPola SThadem SPendyala KVangipuram RVangipuram S(2021)Design and Analysis of activation functions used in deep learning modelsThe 7th International Conference on Engineering & MIS 202110.1145/3492547.3492575(1-5)Online publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1145/3492547.3492575
Cheruvu AVangipuram R(2021)A Survey of Similarity Measures for Time stamped Temporal DatasetsInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460754(193-197)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460754
M.S.B PC.V GVangipuram RCheruvu A(2021)Similarity Association Pattern Mining in Transaction DatabasesInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460752(180-184)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460752
Mortha MMaddala SRaju V(2021)Detection of Text from Video with Customized Trained AnatomyInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460623(12-17)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460623
Mortha MMaddala SRaju V(2021)Data Preprocessing for Learning, Analyzing and Detecting Scene Text Video based on Rotational GradientInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460621(1-8)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460621
Radhakrishna VReddy GKumar PJanaki V(2021)Challenge Paper: The Vision for Time Profiled Temporal Association MiningJournal of Data and Information Quality10.1145/340419813:2(1-8)Online publication date: 13-May-2021
https://dl.acm.org/doi/10.1145/3404198
Aljawarneh SRadhakrishna VCheruvu A(2020)VRKSHA: a novel tree structure for time-profiled temporal association miningNeural Computing and Applications10.1007/s00521-018-3776-732:21(16337-16365)Online publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1007/s00521-018-3776-7
Radhakrishna VKumar PJanaki V(2019)GANDIVAInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.201910010114:4(1-18)Online publication date: Oct-2019
https://doi.org/10.4018/IJITWE.2019100101
Nagaraja AUma BSydykov YAljawarneh S(2019)Intrusion detection using dimensionality reduced soft matrixProceedings of the 5th International Conference on Engineering and MIS10.1145/3330431.3330465(1-7)Online publication date: 6-Jun-2019
https://dl.acm.org/doi/10.1145/3330431.3330465
Radhakrishna VAljawarneh SKumar PJanaki VCheruvu ASydykov YAljawarneh S(2019)Tree based data fusion approach for mining temporal patternsProceedings of the 5th International Conference on Engineering and MIS10.1145/3330431.3330463(1-5)Online publication date: 6-Jun-2019
https://dl.acm.org/doi/10.1145/3330431.3330463
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